FitFunction and Expectation have a bunch of similar methods
that should probably be unified in a single base class.
Other named entities could probably be unified similarly.
There might be a slight performance cost, but it is probably
worthwhile to better organize and simplify the code to ease
maintainance.

Fixed data sorting problem in state space models with lots of help from Tim Brick. Primary change is moving expectationFunctionAddEntities three lines up so that it happens BEFORE convertDatasets in mxRun.R

This should fix the state space example error. I replicated the error on 64-bit Redhat Linux. Adding an upper bound on the offending parameter fixed the issue there. I also found out that state space models do not estimate if you try them multi-threaded.

(Hopefully) fixed the error with the numerous bivariate saturated models. I believe it was a data generation problem with MASS::mvrnorm in which different operating systems returned the data with columns in different orders.

+ Instead of printing exactly what is indigestible, we just deparse the
value. This makes it a little more difficult for users to identify what
went wrong when they pass gibberish into mxModel. For example:

PPML Update: Largely rewritten PPML module with test permutor and new suite of test cases. Functional with the exception of certain cases (optimized numerical solutions to models with data specified in covariance matrix plus means format). Non-functional cases should be caught by the module's filter and allowed to run as normal.

Known Issues:
-Two of the test cases do not pass. One of the failures is possibly due to issues with the conversion from Objectives to Fits/Expectations.
-Tests occasionally find questionable solutions, with very low (negative) Minus2LL values, lower than the minimum found by OpenMx. Allowing the optimizer to try to improve on these questionable solutions throws an error (Expected covariance matrix is non-positive definite).